Statistics for Sustainable Innovation

The Statistics for Sustainable Innovation course is positioned within the Research Methods and Sustainability Assessment Tools learning line, which equips students with knowledge of the scientific methods needed to analyze and assess sustainability-related challenges. By focusing on probability, statistics, and regression modeling, the course builds a strong foundation for interpreting and working with large datasets commonly used in innovation studies. This course directly connects to the broader field of sustainable innovation by providing the analytical skills essential for evidence-based decision-making, whether in academia or industry. Students gain hands-on experience with software tools like STATA, preparing them to apply these skills to real-world problems involving sustainability and innovation.

The first part of the course focuses on probability theory:

• Sample space, events, probability, axioms.
• Probability rules, conditional probability, independence, Bayes' theorem, random variable.
• Discrete distributions, cumulative distribution function, expectation and variance.
• Continuous distributions, density function, mean, variance, correlations and conditional probability

The second part goes more explicitly into statistics. It covers:

• Estimation theory
• The concept of confidence intervals
• The concept of hypothesis testing, t-tests, p-values, Type I and Type II errors, statistical power and size
• Simple linear regression models

In the assignments, the students gain hands-on experience with the data and apply the concepts discussed in class. To do so, they use the STATA statistical package (R is also accepted but no support is offered) and work with data commonly used in innovation studies (Firm-level data, patent data, survey data)

The aim of the course is to provide an introduction to statistics, by focusing on the basics of probability theory and statistics.

After this course the student is able to…

• Apply fundamental probability rules, including conditional probability and Bayes’ theorem.
• Explain the notion of a random variable and concepts such as: discrete distribution and continuous probability distributions (for both including expected value and variance).
• Identify important continuous distributions such as normal and uniform distributions.
• Apply estimation theory to various problems relevant to SI.
• Apply hypothesis testing and construct confidence intervals for making decisions on the difference of means of two normal distributions for paired and independent samples.
• Apply simple linear regression techniques to build empirical models.and assess model adequacy.

You must meet the following requirements

• Registered for a degree programme other than
• HBO-TOP Applied Physics, Pre-Master
• Completed none of the course modules listed below
• Behavioral research methods 2: dwd (0HV50)
• Statistics for IE (2DD80)
• Probability and Statistics for ME (2DW10)

• Lecture slides per week
• D.C.Montgomery, G.C.Runger, Applied Statistics and Probability for Engineers, 7th ed. (ISBN: 978-1-119-58559-6 (printed version) E-book: 978-1-119-40036-3), Wiley.